A manipulationist view of causality in cross-sectional survey research

This essay discusses issues related to establishing causal relationships in empirical survey research. I adopt a manipulationist view of causality because it matches the context of (management) accounting research where we are commonly interested in studying the effects of changes. Strictly speaking, causal relationships cannot be unequivocally proven when the researcher employs cross-sectional surveys—that is, correlation is not causation. Notwithstanding, survey research can be fruitfully engaged to inform pertinent management accounting topics. I discuss four “markers” of causality—theoretical coherence, empirical covariation, temporal/physical separation, and internal validity—and how the researcher can lever these to suggest compelling survey-based inferences. Of these four markers, I particularly emphasize the first as I believe that one piece of any reasonable observer’s considerations will be whether the proffered causal relationships are theoretically plausible. Moreover, a stronger theoretical foundation also helps causal inference by suggesting a reasonably complete set of control variables that are useful to eliminate alternative explanations. Overall, I focus rather pragmatically on the limitations of causal inference when using the survey method and what may be done to try and alleviate, although not eliminate, them.     

Integrating Logical Inference with Numeric Optimization

OR and AI techniques have progressed separately without adequate interactions although they can benefit from each other's complementary strengths: OR in efficient mathematical computation and AI in domain-specific knowledge representation. Constraint Logic Programming (CLP) is introduced as a problem- solving tool which combines these complementary strengths. CLP uses predicate logic for knowledge representation, thus providing stronger expressive powers than prepositional logic, which is employed by mathematical programming. In addition, CLP allows numeric optimization with a computational efficiency comparable to OR approaches. This integration of logical inference and mathematical computation fits well into a special class of decision problems that requires not only logical inference on domain-specific knowledge but also numeric optimization through mathematical formulation. A production planning problem is introduced and solved by CLP. The results are compared with an implementation by mixed integer programming to show the advantages of the integration of OR and AI approaches over either one used alone.     

A rule-based fuzzy reasoning system for assessing the risk of management fraud

Detecting management fraud and assessing the risk of management fraud are significant issues confronting the auditing profession. Considerable theoretical and empirical research (Loebbecke, Eining, and Willingham, 1989; Bell, Szykowny, and Willingham, 1993; Fanning, Cogger, and Srivastava, 1995; and Hansen, McDonald, Messier, and Bell, 1996) has been accomplished investigating these issues. Building on this research, we demonstrate the construction of a rule-based fuzzy reasoning system to assess the risk of management fraud. The paper illustrates how fuzzy sets can be used intuitively to measure red flags on a categorical or interval scale, how different red flags can be combined using fuzzy rules, and how a single measure of the risk of management fraud can be derived. The knowledge base for this fuzzy reasoning system is developed by using the causal model of management fraud developed by Loebbecke, Eining and Willingham (1989), the empirical investigation of this model by Bell, Szykowny, and Willingham (1993), other researchers’ efforts and the authors’ judgments, using XpertRule software. The fuzzy reasoning system is tested using the fraud data provided by KPMG Peat Marwick. We discuss methods to magnify the knowledge base of this fuzzy reasoning system to make it a viable auditing tool, the costs and benefits of building a fuzzy reasoning system, and further extensions of this research. Copyright © 1998 John Wiley & Sons, Ltd.     

Causal inference in empirical archival financial accounting research

This study discusses the challenges and opportunities of establishing causal inference in empirical archival financial accounting research. Causal inference requires identification of a theoretically predicted causal mechanism in a research setting optimized to avoid endogenous causes and using a suitable statistical inference strategy. After briefly describing potential research design strategies, I analyze the frequency of causal studies published in leading business and economics journals. I identify causal studies by their abstract including an explicit reference to their causal nature and find that they are significantly more common in the areas of economics and finance compared to other business-oriented research disciplines like accounting. Also, the extent to which research designs are optimized for causal inference differs significantly between causal empirical archival studies in the area of financial accounting and finance. I discuss potential reasons for this gap and make some suggestions on how the demand for and supply of well-designed causal studies in the area of empirical archival financial accounting research might be increased.     

A Vertical Application Platform for Knowledge-Based Financial Systems

Implementations of knowledge-based systems can be divided into two distinct parts: the knowledge base and the underlying control software. The underlying control software implements reasoning mechanisms and manages data required by the knowledge base. We call this underlying control software a ‘vertical application platform‘. The vertical application platform may be re-used by other knowledge-based applications which have similar reasoning requirements. This paper studies a knowledge-based system called Risk Advisor^sm which aids in the identification and assessment of audit risks. The knowledge roles and reasoning requirements of the knowledge base, and the architecture and implementation of the vertical application platform which implement the necessary reasoning mechanisms are described. The Risk Advisor^sm application platform has in fact been applied with a different knowledge model to other problems in a similar domain.     

Decision making using time‐dependent knowledge: knowledge augmentation using qualitative reasoning

In this paper we propose a method to enhance the performance of knowledge‐based decision‐support systems, knowledge of which is volatile and incomplete by nature in a dynamically changing situation, by providing meta‐knowledge augmented by the Qualitative Reasoning (QR) approach. The proposed system intends to overcome the potential problem of completeness of the knowledge base. Using the deep meta‐knowledge incorporated into the QR module, along with the knowledge we gain from applying inductive learning, we then identify the ongoing process and amplify the effects of each pending process to the attribute values. In doing so, we apply the QR models to enhance or reveal the patterns which are otherwise less obvious. The enhanced patterns can eventually be used to improve the classification of the data samples. The success factor hinges on the completeness of the QR process knowledge base. With enough processes taking place, the influences of each process will lead prediction in a direction that can reflect more of the current trend. The preliminary results are successful and shed light on the smooth introduction of Qualitative Reasoning to the business domain from the physical laboratory application. © 2001 John Wiley & Sons, Ltd.     

The effect of causal knowledge on individuals' perceptions of nonfinancial performance measures in profit prediction

Although organizations commonly report nonfinancial performance measures (NFPMs) associated with profitability, prior research does not address the extent to which the provision of causal knowledge affects individuals' perceptions of the predictive content of such measures. This study examines how providing NFPMs together with different types of causal knowledge (i.e., strong, weak, or none) affects the perceived usefulness of the measures in a profit prediction context. Weak causal knowledge is defined as the direction of the relationship between an NFPM and earnings, while strong causal knowledge is a complete explanation underlying the relationship. The results provide evidence that providing weak causal knowledge increases individuals' perceptions of the predictive content of an NFPM compared to providing no causal knowledge; however, providing strong causal knowledge does not incrementally affect perceptions beyond providing only weak causal knowledge. These findings have implications for the type of information organizations report concurrently with NFPMs.     

Automated Induction of Rule-based Neural Networks from Databases

This paper describes our approach to the problem of automated knowledge acquisition from large databases of examples using an information-theoretic approach. Our previous research has resulted in practical algorithms (ITRULE) for the automatic induction of rules from large example databases. Utilizing these algorithms, the raw data can be transformed into a set of human readable IF THEN rules, thus giving insight into the knowledge hidden within the data. These rules can then be automatically loaded into an expert system shell. Alternatively, they can be used to build a new type of parallel inference system—a rule-based neural network. This process enables a prototype expert system to be automatically generated and up and running in a matter of minutes, compared with months using a manual knowledge-acquisition approach. The resulting expert system can then be used as a sophisticated search and analysis tool to query the original database capable of reasoning with uncertain and incomplete data.     

Adaptive Bayesian Networks for quantitative risk assessment of foreign body injuries in children

Injuries due to foreign body (FB) aspiration/ingestion/insertion represent a common public health issue in paediatric patients, which causes significant morbidity and mortality. The aim of this study is to present a Bayesian Network (BN) model for the identification of risk factors for FB injuries in children and provide their quantitative assessment. Combining a priori knowledge and observed data, a BN learning algorithm was used to generate the pattern of the relationships between possible causal factors of FB injuries. Finally, the BN was used for making inference on scenarios of interest, providing, for instance, the risk that an accident caused by a spherical object swallowed by a male child aged five while playing leads to hospitalization. BNs as a tool for quantitative risk assessment may assist in determining the hazard of consumer products giving an insight into their most influential specific features on the risk of experiencing severe injuries.     

Time-varying pattern causality inference in global stock markets

Causality analysis can reveal the intrinsic interactions in financial markets. Though Granger causality test and transfer entropy method have successfully determined positive and negative causal interactions, they fail to reveal a more complex causal interaction, dark causality. Moreover, the causal relationship between variables may be time-varying. Thus, in this work, we are dedicated to determining the nature of causal interaction and explore the time-varying causality in global stock markets. To achieve this goal, pattern causality (PC) theory, cross-convergent mapping (CCM) theory, the sliding window method and complex networks are applied. By them, three causal interactions with different strength are revealed in global stock markets, and the causal strength is time-varying in different periods both in simulated systems and financial markets. While the dominant causal interaction is stable except for some stock pairs in frontier and emerging markets. In total, we determine the positive dominant causality in global stock markets; that is, the overall consistent trend among stocks can be explored. Additionally, we discover some exceptions that show negative dominant causality, where the reverse trend can be revealed among them; moreover, their dominant causality is time-varying. These uncertainties should receive great attention from investors and government managers.     

Money and economic activity,inventories and business cycles

Incomplete information is a necessary condition for any real effects produced by monetary impulses. An alternative to the local-global inference problem is explored in this paper. Agents are confronted with permanent and transitory shocks. Even with full knowledge about the stochastic structure their best perception at any particular time will usually be erroneous. Prices for each period are set at the beginning of the period on the basis of market conditions. The realization of the shock process thus creates a short-run ‘disequilibrium’ absorbed by inventory adjustments. This adjustment translates perceived transitory monetary shocks into serially correlated output movements. The analysis proceeds within the context of rational expectations It offers a generalization of equilibrium analysis in two respects. Prices are always in equilibrium relative to perceived conditions, but they do not reflect all ongoing shocks. Quantity adjustments reflect the perceived transitory shocks. The framework used involves moreover a stock-flow interaction operated by inventory adjustments. The stock-flow interaction imposes at any time a future expected adjustment path (for price-level and quantities) to the system's unique stock equilibrium. A major implication of the analysis resolves a puzzle experienced in a recent paper by Robert Hall. It reconciles intertemporal substitution with lagged effects of monetary impulses. It also reconciles small and inconclusive cyclic movements in real wages with the occurrence of production function and large variations in unemployment. Lastly, the nature of the inference problem determined by the pattern of incomplete information produces serially correlated movements conditioned on large permanent shocks.     

Strengthening causal inferences in positivist field studies

This essay discusses how incorporating qualitative analyses and insight in positivist field studies can strengthen researchers’ ability to draw causal inferences. Specifically, I review how the rich institutional knowledge available in field settings can be used to increase internal validity by improving the specification of empirical models and tests and by providing greater insight into statistical results, particularly through the investigation of the causal processes linking the accounting practices and outcomes.     

Role of digital finance,investment, and trade in technological progress

The primary purpose of this study is to evaluate the impacts of digital finance, trade, and investment on technological progress in 30 developing Asian economies by employing the FMOLS (Fully Modified OLS) technique and annual data from 2000 to 2020. The findings concluded that digital finance has a positive impact on technology progress. Moreover, trade liberalization may accelerate technological progress. Foreign direct investment has no significant coefficient, interpreting that inward FDI (Foreign Direct Investment) to the examined developing Asian economies is unrelated to technological progress. The causality test confirmed a neutrality hypothesis for the causal relationship between FDI and technological progress. Furthermore, the bi-directional linkage between trade openness and technological progress is confirmed. The paper recommends that developing economies increase the share of investment in technological progress, prioritization of regionalism and trade multilateralism for technology transfer, and establishment of a “digital financial inclusion” framework.     

Exploring the causal relationships and allocation puzzle between portfolio investments and real sector growth in Sub-Saharan Africa

This study explores the causal dynamics and allocation puzzles between real sector growth and portfolio investments (equity and private debt) in Sub Saharan (SSA). We use growth in manufacturing, industry, agriculture, and services to capture the real sector and employ the two-step dynamic systems GMM model to establish our empirical relationships over the period 1980–2017. We found no evidence in support of the puzzle at the overall level of the real sector and portfolio investments. However, at a decomposed level, we established a bi-directional relationship of a positive association between debt flows and growth in agriculture and services, with no evidence of an allocation puzzle. Though we found a bi-directional association between debt and industrial growth, the association was detrimental in both directions. Finally, the study established a two-way inverse causality between equity flows and manufacturing growth. The paper provides a strong foundation for an additional source of financing, especially for the growth of the service and agriculture sectors. The findings also indicate complementary reactions between real sector growth and portfolio investments in SSA.     

Cutting to the core? A reflection upon recent education policy debates within the Institute of Chartered Accountants in England and Wales

Knowledge is widely regarded as a characteristic of professions, but given the ever-increasing knowledge base, professional accountancy bodies have begun to question whether professional examinations can continue to cover all areas relevant to the work of accountants. At the heart of the debate about how the professional knowledge base is to be defined lie questions about how chartered accountants are ‘made’. This paper discusses the introduction of a ‘core and options’ model for professional accountancy education as a possible means of ‘making the chartered accountant’. Using Porter's (1981) theory of historical explanation, it discusses an episode in the recent history of education policy at The Institute of Chartered Accountants in England and Wales (ICAEW) which illuminates aspects of the core and options model. The paper concludes that, while the introduction of core curricula in accounting education can be justified on educational grounds, the rejection of core curricula by ICAEW suggests that educational debates were strongly influenced by the wider political, economic, social and professional environments and the resultant educational policy can be viewed as the product of a variety of competing agendas.     

Combining Expert Systems and Hypertext: Opportunities and Obstacles

Hypertext discussions are occurring more frequently at expert systems conferences. Hypertext is not an expert system language or expert system shell, but instead it is simultaneously a method of storing and retrieving data. The growing interest in hypertext in the expert system domain is because the combination of hypertext capabilities and expert systems creates a compelling synergistic relationship (Oren, 1987). During a panel discussion at the Second International Symposium on Expert Systems in Business, Finance and Accounting, Bill Swartout compared traditional expert systems to hypertext by saying that the knowledge base (e.g. the production rules) of an expert system can be thought of as formal knowledge and hypertext can be thought of as informal knowledge. This article will demonstrate how this informal knowledge can add power to traditional expert systems by increasing the flexibility of the explanation facilities and thereby the appeal of the system to a broader range of users. The article also will discuss how expert systems can improve the functionality of hypertext systems by adding intelligence to traditional hypertext systems. This article is divided into three sections. The first describes basic hypertext concepts. The second section shows how hypertext can enhance expert systems and, conversely, how expert systems can enhance hypertext systems. The final section discusses some potential problems and concerns that must be considered when designing a hypertext system.     

Evaluating business credit risk by means of approach‐integrating decision rules and case‐based learning

This research utilizes a new approach which uses a hybrid learning system that combines two representations of knowledge: the first in a form of decision rules referring to general knowledge, and the other of single cases corresponding to exceptions or untypical situations. The Explore algorithm was chosen as a tool for inducing general rules. It generates all simple and sufficiently strong general rules from a given data set. Examples discovered by these rules are then used to identify exceptions and untypical cases. The paper discusses problems connected with tuning parameters of this approach and introduces a new procedure for this task. This methodology is applied to solve the problem of evaluating the risk of business credit applications in a Polish commercial bank. Using information about business credit applications, as described by 35 economic parameters and using five groups of banking risk, a knowledge base consisting of 70 decision rules and 15 specific cases was induced. Testing this model in the standard ‘leaving‐one‐out’ way we achieved the best classification accuracy of 81%. A comparative study showed that results obtained by other machine‐learning algorithms resulted in significantly worse classification accuracy. Copyright © 2001 John Wiley & Sons, Ltd.     

Predictive regression: An improved augmented regression method

This paper proposes three modifications to the augmented regression method (ARM) for bias-reduced estimation and statistical inference in the predictive regression. They are in relation to improved bias-correction, stationarity-correction, and the use of matrix formulae for bias-correction and covariance matrix estimation. The improved ARM parameter estimators are unbiased to the order of n^− 1, and always satisfy the condition of stationarity. With the matrix formulae, the improved ARM can easily be implemented for a high order model with multiple predictors. From an extensive Monte Carlo experiment, it is found that the improved ARM delivers substantial gain in parameter estimation, statistical inference, and out-of-sample forecasting in small samples. As an application, the improved ARM is applied to monthly US stock return data to evaluate the predictive power of dividend yield in univariate and bivariate predictive models.     

Nexus analysis of financial management,digital finance and new technologies

This paper measures the associations between financial development, green finance, and technological progress, focusing on developing countries in Asia and Europe. Employing the FMOLS estimation of variables from 2010 to 2021 concluded that the digital finance index has a positive coefficient in the panel of developing countries and the panel of European developing countries, while the coefficient of this variable is insignificant for Asian developing countries. The financial depth index also has a positive coefficient in all three-panel groups of countries. Regarding the Asian developing economies, there is only a bi-directional causal relationship between the innovation index and financial development. Developing countries can implement developing digital finance markets, improving good governance, increasing the competitiveness index, and creating a regional digital finance network as practical policies.